Two years ago, I sat down for an interview about the future of data in clinical research . I spoke about the shift from siloed information to interconnected data ecosystems, the power of real-world data, and the need for a more thoughtful, rigorous approach to data science. At the time, I was in a global role at a major pharmaceutical company, working to drive this transformation from the inside.
Now, in 2026, I have a different perspective. As an independent advisor, I see the same challenges from a new vantage point. The urgency is greater, the resources are often scarcer, and the need for clarity is more critical than ever. The core principles I discussed back then haven’t just held true, they’ve become the absolute bedrock of any successful data strategy.
What has become even clearer is that our professional struggles with data chaos, information overload, and a lack of clear systems are not just business problems. They are human problems. They mirror the exact same challenges we face in our personal lives when we feel overwhelmed, disorganized, and pulled in a thousand directions.
Building a successful data ecosystem at an organization requires the same mindset shift as building a harmonious, productive life. It’s not about more tools, more dashboards, or more data. It’s about building a system so clear and so intuitive that the technology becomes invisible. It just works.
Here are the lessons that have only become sharper with time.
Lesson 1: Treat Data as a Company Asset, Not an Exhaust Fume
For decades, most organizations have treated data as the byproduct of a process. It’s the exhaust fume of a clinical trial, a sales transaction, or a marketing campaign. It gets stored somewhere, often in a format that’s difficult to access or understand, and is rarely revisited with a strategic purpose after it’s primary purpose is delivered.
This has to change, and for some, it has started to change already. The single most important mindset shift is to start treating data as a core company asset. Like any asset, it must be managed, governed, and leveraged to create future value, or, where appropriate, to intentionally be designed for a single, value-adding point.
Fundamentally, this means asking different questions:
- Instead of: “Where do we store this data?”
- Ask: “How can we make this data findable, accessible, interoperable, and reusable (FAIR) for future projects?”
- Instead of: “Did we collect the data for this specific study?”
- Ask: “What other questions could this data help us answer, and how can we combine it with other assets to generate new insights?”
This isn’t just a semantic change. It’s a fundamental re-orientation that forces discipline, encourages long-term thinking, and turns a cost center into a value-generating engine.
Lesson 2: Rigor is the Highest Form of Speed
Our industry is obsessed with speed. We have tools that allow us to run a thousand analyses in an afternoon. But as a wise statistician once reminded me, there was a time when every analysis cost money and computational resources. You had to be deliberate. You had to think.
Nowadays, it’s so easy: you can just go in, you can do an analysis and you can come out and walk away. But we have to still be so careful; we cannot do this in the absence of thinking.
We’ve mistaken the ability to get a fast answer with the ability to get the right one. The most powerful data-driven organizations I see are not the fastest in a reactive sense. They are the most thoughtful. They are rigorous about understanding the source of their data, its limitations, and its biases. They design their inquiries with intention.
This thoughtful approach feels slower at the start, but it is ultimately faster. It prevents the costly mistakes, the chasing of false signals, and the erosion of trust that comes from rushing to conclusions. Rigor isn’t the enemy of speed; it’s the foundation of sustainable velocity.
Lesson 3: The Best Technology is Invisible
From the OMOP common data model to the FAIR principles, our industry has made strides in creating standards for data interoperability. But the goal of these standards isn’t to create a more complex technical landscape. The goal is to make the technology disappear.
Think of the best consumer technologies you use. They just work. You don’t think about the complex ecosystem of APIs, data models, and infrastructure that makes them possible. You just get the answer you need.
That is the ultimate vision for a data ecosystem in any organization. It should be as easy as a Google search to find the information you need. It should feel intuitive to combine data from different sources. The system should be so well-designed that the user can focus on the question, not the tool.
This is where my work in professional data systems connects directly to the principles of personal productivity I teach in Harmony at Home. A well-organized life, like a well-organized data ecosystem, isn’t about having the fanciest apps or the most complicated to-do lists. It’s about creating a simple, reliable system that you trust completely, so you can free up your mental energy to focus on what truly matters.
Whether it’s a clinical researcher trying to understand a patient’s journey or a parent trying to manage a busy household, the goal is the same: clarity, confidence, and the freedom to do your best work.
We have a long way to go, but the path forward is clear. It requires a shift in mindset, a commitment to rigor, and a relentless focus on the human at the center of the system. It’s not just about shaking up how we think about data. It’s about building systems that help us think better, together.
References
This post is adapted from the interview I did. You can find the original interview in the link below.
[2] Learn more about FAIR Principles
[3] Learn more about the OMOP Common Data Model
About the Author:
Dr. Victoria Gamerman is a biostatistician, digital transformation consultant, and thought leader specializing in AI and real-world evidence applications in life sciences. After over 10 years leading global digital transformation and data science at Boehringer Ingelheim, where she built real-world evidence strategies, led data science initiatives, and designed data governance frameworks for AI delivery, she continues to lecture at Columbia and mentor startups at NYU while running RWD Insights, her consulting practice focused on helping life science oriented companies implement AI responsibly and effectively. She is a 2025 recipient of the WLDA Trailblazer in Responsible Innovation Award.
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